Generally, annotating pixel-level segmentation labels is time-consuming compared with annotating classification labels. Moreover, we observe that there is an additional set only designed for classification tasks consisting of 1,320 skin images, which are widely used in the previous methods [1]}, [2]} to boost the performance of classification. On the contrary, segmentation labels are not available in this part, which puts obstacles on training such a multi-task framework. One naive solution is that we only need to optimize the classification branch when training on the additional part. However, we suggest that the additional dataset can indeed provide cues for the segmentation tasks by leveraging dual-task consistency between segmentation mask \(M\)  and level set function \(L\) . Empirically, a robust model is capable of achieving high consistency between correlative predictions [3]}.
